An Examination Of The Evolution Of An Education Management Information System From A Sensemaking Viewpoint And The Use Of Quantitative Methods To Assess Educational Datasets
DOI:
https://doi.org/10.63682/jns.v13i1.7174Keywords:
Data Sets for Education, Educational Administration, Sensemaking Framework, Electronic Management Information Systems (EMIS)Abstract
In this study, researchers use quantitative methods to examine educational datasets. The development process and impacts of the "Education Management Information System (EMIS)" are examined using sensemaking. The motivation for this study was to enhance data use and the capacity of EMISs to support educational decision-making. Researchers thoroughly examine EMISs from start to finish because of the many stakeholders whose capabilities they affect. Stakeholders include lawmakers, administrators, and educators. Researchers evaluate the impact of the EMIS on stakeholders' data understanding and strategic application using a sensemaking approach. For this, they will need to track how users interact with the system and determine if it can back up data-driven decisions. Simultaneously, the study employs quantitative approaches to examine educational data sets managed by the EMIS. Finding out how these quantitative analyses contribute to bettering educational outcomes and policy decisions is an important aspect of this process, as is ensuring that the data is accurate, comprehensive, and usable. Data integrity, data relevance, and the impact of data-driven decisions on instructional methods are important performance indicators. In order to make students more at ease with quantitative methods and improve their sensemaking skills in class, the findings should suggest ways to improve the design of EMIS. A more data-informed and efficient approach to school administration is the overarching aim of the project, which aims to unite diverse perspectives in this regard. In the end, this should lead to better academic performance.
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Preece, A., Braines, D., Cerutti, F., Pearson, G., & Kaplan, L. (2021). Coalition situational understanding via adaptive, trusted and resilient artificial intelligence analytics. NATO Science & Technology Organization. STO-MP-IST-190.
Sandberg, J., & Tsoukas, H. (2020). Sensemaking reconsidered: Towards a broader understanding through phenomenology. Organization Theory, 1(1), 1–34.
Sbaffi, L., & Hargreaves, S. (2022). The information trust formation process for informal caregivers of people with dementia: A qualitative study. Journal of Documentation, 78(2), 302–319.
Schildt, H., Mantere, S., & Cornelissen, J. (2020). Power in sensemaking processes. Organization Studies, 41(2), 241–265.
Terry-Bowles, M., & Sobel, K. (2022). Librarians as faculty developers: Competencies and recommendations. Journal of Academic Librarianship, 48(1), 102474.
Turner, J. R., Allen, J., Hawamdeh, S., & Mastanamma, G. (2023). The multifaceted sensemaking theory: A systematic literature review and content analysis on sensemaking. Systems, 11(3), 145.
Urban, A. C. (2021). Narrative ephemera: Documents in storytelling worlds. Journal of Documentation, 77(1), 107–127.
Urquhart, C., & Lam, L. (2021). Dervin's sense-making methodology. In I. Fourie (Ed.), Autoethnography for librarians and information scientists (pp. 112–127).
Valentine, L., McEnery, C., O'Sullivan, S., Gleeson, J., Bendall, S., & Alvaraz-Jimenez, M. (2020). Young people's experience of a long-term social media-based intervention for first-episode psychosis. Journal of Medical Internet Research, 22(6), e17570.
Van der Merwe, S. E., Biggs, R., Preiser, R., Cunningham, C., Snowden, D. J., O'Brien, K., Jenal, M., Vosioo, M., Blignaut, S., & Goh, Z. (2019). Making sense of complexity: Using SenseMaker as a research tool. Systems, 7(2), 25.
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